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Artificial Intelligence (A.I) in Higher Education: opportunities, risks and Assessment Design
Oct 24, 2025
A short lecture on Artificial Intelligence in Higher Education. It begins with a definition, the applications of AI, the risks and limitations, how assessment design has to change and the way forward.
WEBPAGE: https://academic-englishuk.com/ai-in-education/
LECTURE QUESTIONS DOWNLOAD: https://academic-englishuk.com/ai-in-education/
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0:04
Hello and welcome to this short lecture
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on AI in higher education. Today we'll
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specifically look at AI opportunities,
0:13
risks and assessment. So we'll start
0:15
with the definition followed by AI
0:18
applications, then limitations, then on
0:21
to how assessments will have to change
0:23
to become more resilient, and finally a
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conclusion of final thoughts and a
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question to ponder.
0:31
Just before we begin this lecture, I'd
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like to highlight that this research was
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created by analyzing current guidance
0:38
and evidence from the UK government
0:40
alongside policies and practice papers
0:43
from leading UK universities.
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All sources will come at the end of the
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lecture.
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So let's begin with the definition.
0:53
Generative artificial intelligence or
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Gen AI refers to tools like chat GPT,
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Copilot, Deepseek, Dowo, and Grammarly
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that can create new content such as
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texts, images, code, and simulations. In
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education, these tools can help students
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by providing instant feedback,
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personalized study support, and
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realistic practice scenarios.
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However, they also present challenges.
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For example, they can spread false
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information, raise ethical concerns, and
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reduce genuine critical thinking if used
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without care or proper evaluation.
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Just so we're clear from the start, AI
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is an umbrella term for all forms of
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artificial intelligence. While
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generative AI is a specific type of A, I
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designed to produce new data, not just
1:46
process or recognize it. In this
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lecture, I will primarily refer to
1:50
generative AI, but at times I will
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simply use the term AI to mean the same
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thing.
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So then, how prevalent is AI in higher
2:01
education? A recent statistic from the
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Oxford Higher Education Policy Institute
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who carried out an online questionnaire
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with a representative sample of 1,041
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undergraduates from across UK higher
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education found that 88% of UK
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university students admitted to using
2:20
generative AI tools for assessments.
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So what do students use AI for at
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university?
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AI tools can enhance students learning
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by offering personalized support,
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creative ideas, and instant feedback.
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The following examples show how AI can
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be used to develop understanding,
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improve productivity, and make studying
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more interactive and inclusive.
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One, it helps with personalized learning
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where AI tools adapt study pace, review
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key topics, and focus on areas students
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find difficult, or content exploration
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to generate study notes, quizzes,
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summaries, and multimedia explanations
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to support understanding. There's
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virtual tutoring where students can ask
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AI for one-to-one explanations,
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examples, or feedback when revising or
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preparing assignments.
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Two, it can create simulations and
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scenarios to help practice real world
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situations or case studies to apply
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theory to practice.
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Three, it can help with assessment and
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feedback to use for self-checking,
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receiving formative feedback or tracking
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your learning progress.
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Four, it will improve accessibility to
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get complex texts simplified,
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translated, or converted to accessible
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formats such as alt text or audio.
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Five, it can be used for text
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manipulation to summarize readings,
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paraphrase key points, or reformat notes
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into tables or outlines.
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Six, of course, there is idea generation
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to brainstorm essay topics, argument
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structures, or report outlines to plan
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your writing. And that would include
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data analysis and visualization where it
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can turn data into clear charts, graphs,
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or infographics for presentations or
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reports.
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And finally, seven, productivity support
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to organize tasks, plan study schedules,
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and manage time more efficiently.
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So although there are many benefits, it
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is not entirely without flaws.
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Let's now look at the limitations and
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risks of generative AI in education.
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The most serious issue is reliability
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and accuracy.
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AI often produces errors or misleading
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outputs because it cannot reliably
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distinguish truth from falsehood. In
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some cases, it fabricates information
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known as hallucinations, including false
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references that appear credible but do
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not exist. Performance can also decline
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over time through a process called
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algorithmic drift. At present, this
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occurs when accuracy and reliability
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decrease because the system continually
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repeats and recycles the same
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information, reinforcing patterns and
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errors rather than adapting to new or
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changing data.
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Now, let's turn to the second serious
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issue, which is bias and fairness.
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AI systems are trained on vast data sets
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that may include stereotypes, cultural
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biases, or discriminatory patterns,
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which are often reproduced and even
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amplified in their outputs. There is a
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lack of transparency in how these
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systems generate responses, often
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described as the blackbox problem, which
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limits accountability and a clear
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insight into decision-m processes.
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Generally, AI presents information in a
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confident and authoritative manner which
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can encourage users to accept flawed or
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misleading content without applying
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sufficient critical evaluation.
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Now, the third point is academic
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integrity.
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By producing work with AI that does not
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reflect their own understanding,
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students risk committing plagiarism or
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academic dishonesty and graduating
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without genuine expertise or
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professional competence. In fact, such
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excessive reliance contradicts
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university pedagogical principles as it
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weakens critical thinking, problem
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solving, and independent learning
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skills.
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Then of course there's data privacy and
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compliance.
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AI systems often process sensitive
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personal or research data, raising risks
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of breaches, surveillance, or misuse.
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Involving third party vendors, which are
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external organizations that store,
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manage, or analyze data on behalf of an
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institution introduces additional risks
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of interception, unauthorized access, or
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exposure outside the institution's
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direct control. Many AI systems are
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trained on copyrighted material without
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permission, which raises questions about
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intellectual property, unclear ownership
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of outputs, and potential legal
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disputes. Furthermore, accessibility and
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equality requirements must be addressed
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to ensure full compliance with General
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Data Protection Regulation, GDPR, and
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Data Protection Act, DPA obligations.
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And finally, there is ethical and social
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concerns.
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AI outputs may conflict with
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institutional values or misalign with
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ethical standards. In particular,
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overuse of AI where automated systems
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are relied upon more than human judgment
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can restrict academic freedom and
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discourage independent perspectives.
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This dependence may also reduce
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creativity and undermine professional
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expertise by replacing original thought
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and specialist knowledge with formulaic
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outputs.
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So bearing all of that in mind,
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educators will need to start to think
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about designing AI resilient
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assessments.
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AI resilient assessment design will now
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need to combine selective invigilation,
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live components, contextualized and
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process-based tasks, collaboration,
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ethical judgment, experiential learning,
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and guided AI used to promote integrity,
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critical thinking, and authentic
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engagement. Let's look at these in more
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detail.
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Number one, invigilated and onampus
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components.
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This will be to incorporate supervised
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or invigilated elements such as
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in-person exams, vivvers, or practical
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demonstrations to confirm authorship and
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understanding.
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Number two, contextual and localized
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assessment tasks.
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Assignments will have to draw on module
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specific readings, seminars or case
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studies.
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Also, it may require reference to local
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data examples or institutional contexts
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that AI systems are less able to
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replicate accurately.
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Number three, interlin and developmental
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assessment.
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Students will be required to create
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portfolios or linked assessments across
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a module or program to be assessed on
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coherence and sustained learning.
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This could include asking students to
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provide annotated drafts, research
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notes, or reflective commentaries that
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demonstrate process and progression.
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Number four is higher order thinking and
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critical engagement.
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An emphasis on these skills such as
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analysis, synthesis, and evaluation in
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both task design and marking rubrics
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will be required,
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especially to use criteria that rewards
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original argumentation, integration of
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theory, and engagement with course
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materials.
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Number five, authentic and
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scenario-based tasks.
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This will be framing assignments around
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realistic case studies, simulations, or
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applied problems that include specific
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constraints, defined time limits,
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limited data sets, local examples, or
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scenario-based conditions.
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Again, opportunities for personal
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insight or experience connected to
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academic evidence and theoretical
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frameworks will be necessary.
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Number six, incorporation of AI within
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assessment.
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This will be become more prevalent as AI
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grows and will require students to
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critically evaluate AI outputs or AI
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produced work.
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So this now leads me into how
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universities are dealing with AI at the
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moment.
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One new approach being introduced in
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assessment and task guides is the use of
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a coding system that tells students when
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and how they are permitted to use AI.
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Some universities are now using a
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traffic light assessment system like
11:41
this one on the slide. It highlights a
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clear policy on academic integrity by
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defining when AI use is prohibited red
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allowed in an assistive role amber or
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required as part of the assessment
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green. It ensures students use AI
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responsibly supporting learning without
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undermining originality, fairness or
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academic standards.
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Interestingly, traditional pen and paper
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tests are making a comeback and becoming
12:12
more common as universities respond to
12:15
AIUS.
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The University of Liverpool emphasizes
12:19
that assessments must never replace
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original thought, independent research,
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and the production of original work,
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recommending traditional invigilated
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handwritten formats as part of a wider
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strategy to safeguard authenticity and
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uphold academic integrity.
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And Newcastle University recommends
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using assessment formats that are less
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vulnerable to AI misuse, such as
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invigilated in-person pen and paper
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exams, to preserve academic integrity in
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a world where generative AI is widely
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accessible.
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This slide shows a range of paper and
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pen tests with the test type, what it
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assesses, and how it resists AI. Have a
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good look at how these tests are
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assessing understanding and knowledge.
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Okay, now to the final conclusion.
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So to conclude, generative AI is rapidly
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transforming higher education, offering
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powerful opportunities to enhance
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learning, personalize feedback, and
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improve accessibility. Yet, as we have
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seen, it also introduces significant
13:34
risks related to accuracy, ethics, and
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academic integrity. Universities are
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therefore rethinking how they design
13:43
assessments to protect the core values
13:45
of higher education, genuine
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understanding, originality, and
13:49
fairness.
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As we move forward, the challenge is not
13:55
to resist AI, but to integrate it
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responsibly, ensuring that technology
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supports rather than replaces human
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judgment, creativity, and independent
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thought.
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I'll leave you with a final question to
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reflect on.
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Is generative AI helping us learn more
14:14
deeply or simply making it easier to
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appear as though we have?
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Here is the reference list for this
14:23
lecture.
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Thank you and good evening.
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